WO2021175423A1 - Contrôle prédictif basé sur un modèle d'un véhicule prenant en compte un facteur de temps d'arrivée - Google Patents

Contrôle prédictif basé sur un modèle d'un véhicule prenant en compte un facteur de temps d'arrivée Download PDF

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Publication number
WO2021175423A1
WO2021175423A1 PCT/EP2020/055771 EP2020055771W WO2021175423A1 WO 2021175423 A1 WO2021175423 A1 WO 2021175423A1 EP 2020055771 W EP2020055771 W EP 2020055771W WO 2021175423 A1 WO2021175423 A1 WO 2021175423A1
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WO
WIPO (PCT)
Prior art keywords
vehicle
processor unit
trajectory
arrival time
account
Prior art date
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PCT/EP2020/055771
Other languages
German (de)
English (en)
Inventor
Valerie Engel
Andreas Wendzel
Michael Wechs
Maik DREHER
Lorenz Fischer
Oliver Schneider
Christian Baumann
Edgar Menezes
Felix SPURA
Original Assignee
Zf Friedrichshafen Ag
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Filing date
Publication date
Application filed by Zf Friedrichshafen Ag filed Critical Zf Friedrichshafen Ag
Priority to US17/776,946 priority Critical patent/US20220402476A1/en
Priority to CN202080079801.5A priority patent/CN114746316A/zh
Priority to PCT/EP2020/055771 priority patent/WO2021175423A1/fr
Publication of WO2021175423A1 publication Critical patent/WO2021175423A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W20/00—Control systems specially adapted for hybrid vehicles
    • B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
    • B60W20/11—Controlling the power contribution of each of the prime movers to meet required power demand using model predictive control [MPC] strategies, i.e. control methods based on models predicting performance
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/0097—Predicting future conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3469—Fuel consumption; Energy use; Emission aspects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0037—Mathematical models of vehicle sub-units
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/10—Buses
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/12—Trucks; Load vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2300/00—Indexing codes relating to the type of vehicle
    • B60W2300/12—Trucks; Load vehicles
    • B60W2300/125—Heavy duty trucks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
    • B60W2555/60—Traffic rules, e.g. speed limits or right of way
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2720/00—Output or target parameters relating to overall vehicle dynamics
    • B60W2720/10—Longitudinal speed
    • B60W2720/103—Speed profile

Definitions

  • the invention relates to the model-based predictive control of a vehicle, taking into account at least one arrival time factor.
  • a processor unit a driver assistance system
  • vehicle a vehicle
  • method a computer program product
  • Model Predictive Control in English: Model Predictive Control or MPC for short
  • MPC Machine Control
  • a driving strategy is usually calculated in a model-based predictive control of a vehicle for a limited forecast horizon.
  • an arrival time of the vehicle at the destination cannot be predicted and a trajectory of the vehicle cannot be optimized accordingly.
  • fuel stops and vehicle charging stops are not taken into account.
  • An object of the present invention can be seen in providing an improved MPC control of a drive train of a motor vehicle.
  • the object is achieved by the subjects of the independent claims.
  • Advantageous embodiments are the subject matter of the subclaims, the following description and the figures.
  • the present invention provides energy efficient and time efficient route planning.
  • an optimization of a driving speed of a vehicle for an optimal energy consumption and / or an optimal driving time to a desired local destination is proposed.
  • further information and influences on the arrival of the vehicle at the desired local final destination, such as necessary break times are taken into account.
  • the optimization takes place for purely electric vehicles or plug-in hybrid vehicles, which means that the use of electric charging stations is not absolutely necessary and a further degree of freedom for the optimization problem arises.
  • the model predictive control of a vehicle is expanded according to the present invention by calculating the complete route and trajectory, taking into account arrival time factors that influence the travel time or the arrival time of the vehicle at a desired local destination.
  • arrival time factors are break times (private person, truck driver, bus driver), times for loading a truck, times for refueling / charging the battery, information about the availability of charging stations, traffic volume and traffic jams and weather conditions on the route.
  • the driving speed and energy consumption can also be calculated in advance, taking into account the specification of a desired arrival time or a desired range.
  • a processor unit for model-based predictive control of a vehicle, taking into account an arrival time factor.
  • the processor unit is set up to calculate a trajectory for the vehicle, taking into account at least one arrival time factor.
  • the trajectory includes or takes into account an entire route up to a specified destination at which the vehicle is to arrive.
  • the arrival time factor influences an arrival time of the vehicle at the specified destination.
  • the trajectory for the vehicle calculated in this way can be relatively coarse, with no gears being predefined for the vehicle, for example.
  • the specified destination is a position at which the vehicle should arrive. Starting from a starting position, the route leads to the destination.
  • the "entire route" comprises the entire route between start and finish.
  • the prediction horizon of the model-based predictive control is sliding and covers part of the entire route until the destination is reached.
  • the destination can be specified by a driver of the vehicle, for example by an input on a navigation device of the vehicle.
  • the goal can also be, for example can be specified by an external unit, for example by a command center of a vehicle fleet arranged outside the vehicle.
  • the trajectory for the vehicle can be generated, for example, by a route algorithm that can be embedded in a system for autonomous driving (common English term: Autonomous Driving System, or AD system for short).
  • the AD system typically includes a module for ambient perception with sensors and a planning module.
  • the planning module can comprise several levels, e.g. a navigation level on a larger scale (e.g. several km; the vehicle trajectory from start to destination can be selected or calculated here) and a navigation level on a smaller scale (e.g. in the 50 m to 100 m ahead) m, depending on the speed of the vehicle; a choice of course and speed in the immediate vicinity of the vehicle is possible here in order to determine how the vehicle should move in traffic).
  • the processor unit is also set up to optimize a section of the trajectory for the vehicle for a sliding prediction horizon by executing an MPC algorithm which contains a longitudinal dynamics model of a drive train of the vehicle and a cost function to be minimized, so that the cost function is minimized will.
  • the optimized trajectory for the sliding prediction horizon is finer than the speed trajectory for the entire route and can, in particular, specify gears for the vehicle.
  • the calculation of the vehicle trajectory for the entire route is therefore carried out separately from the MPC-based optimization of the vehicle trajectory within the sliding prediction horizon.
  • a new or updated section of the vehicle trajectory is optimized for the entire route for each new prediction horizon.
  • the processor unit can form the planning module (“top lever planning module”) described above, which plans the entire vehicle trajectory, taking into account the arrival time factors.
  • This planning module can then transfer parts of the entire vehicle trajectory to the MPC algorithm, by means of which an optimal trajectory of the vehicle within the prediction horizon is can be averaged.
  • the planning module can set a suitable point at the end of the section that is transferred to the MPC algorithm, which corresponds to the target time at the end of the section.
  • the processor unit can be set up to control the vehicle, in particular an electrical machine of the vehicle, based on the determined, optimized trajectory.
  • the processor unit can be set up to transmit the trajectory for the vehicle, which has been optimized by means of the MPC algorithm, to a target generator, which can in particular be implemented by a software module.
  • the processor unit can convert the mathematically optimal trajectory for the vehicle into actually usable component signals.
  • a speed trajectory of the vehicle can be optimally planned for the next 500m using the MPC control.
  • the target generator would use the manipulated variables or target variables from the trajectory that correspond to the current point in time.
  • the trajectory for the vehicle determined and optimized by the processor unit can in particular be used to provide an autonomous or at least partially autonomous driving function for the vehicle.
  • This driving function can be provided by a driver assistance system of the vehicle.
  • the autonomous driving function enables the vehicle to drive independently, ie without a vehicle occupant controlling the vehicle. The driver has given control of the vehicle to the driver assistance system.
  • the autonomous driving function includes that the vehicle is set up - in particular by means of the central processor unit - to carry out, for example, steering, blinking, acceleration and braking maneuvers without human intervention and, in particular, to control exterior lights and signals such as the vehicle's blinkers.
  • the semi-autonomous driving function can be understood as a driving function that supports a driver of the vehicle in controlling the vehicle, in particular during steering, blinking, acceleration and braking maneuvers, the driver still having control of the vehicle.
  • the method of model-based predictive control (MPC) was chosen in order to find an optimal solution for a so-called “Driving Efficiency” function in every situation under given boundary conditions and restrictions.
  • the MPC method is based on a system model that describes the behavior of the system. Furthermore, the MPC method is based on a target function or a cost function that describes an optimization problem and determines which state variables are to be minimized.
  • the state variables for the Driving Efficiency driving function can in particular be the vehicle speed or the kinetic energy, the remaining energy in the battery and the driving time.
  • the optimization of energy consumption and travel time takes place in particular on the basis of the gradient of the route ahead and restrictions for speed and drive power, as well as on the basis of the current system status.
  • the target function or the cost function of the Driving Efficiency driving strategy can in particular contain two terms by which both energy consumption and driving time to the specified destination are minimized. This leads to the fact that, depending on the choice of weighting factors that can be assigned to the terms, a low speed is not always evaluated as optimal and it can thus be avoided that the resulting speed is always at the lower limit of the permitted speed.
  • the present invention also makes it possible that the driver's influence no longer has to be relevant for the energy consumption and the driving time of the motor vehicle, in that in particular an electrical machine for driving the vehicle can be controlled by the processor unit based on an input variable for the electrical machine, which is determined by executing the MPC algorithm.
  • an optimal engine operating point of the electrical machine can be set by means of the input variable.
  • an optimal adjustment of the optimal speed of the motor vehicle can take place.
  • the longitudinal dynamics model of the drive train can include a vehicle model with vehicle parameters and drive train losses (partly approximated maps).
  • knowledge of the route topographies e.g. curves and gradients
  • knowledge of speed limits on the route ahead can also flow into the longitudinal dynamics model of the drive train.
  • the cost function can only contain linear and quadratic terms. As a result, the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved quickly and easily.
  • the target function or the cost function can be set up with a weighting (weighting factors), in which case, in particular, energy efficiency, travel time and travel comfort are calculated and weighted.
  • An energy-optimal speed trajectory can be calculated online for a horizon lying ahead on the processor unit, which can in particular form a component of a central control unit of the motor vehicle.
  • the setpoint speed of the motor vehicle can also be recalculated cyclically on the basis of the current driving state and the route information ahead.
  • route data from an electronic map for the sliding forecast horizon or prediction horizon (for example 500 m) in front of the vehicle can be updated or updated, in particular cyclically.
  • the route data can contain, for example, gradient information, curve information, and information about speed limits.
  • a curve curvature can be converted into a speed limit for the motor vehicle using a maximum permissible transverse acceleration.
  • the motor vehicle can be localized, in particular via a GNSS signal for precise localization on the electronic map.
  • the cost function of the MPC algorithm can be used to minimize, in particular, the travel time for the prediction horizon and to minimize the energy consumed.
  • the MPC algorithm can be supplied with additional constraints such as speed limits, physical limits for the torque and speeds of the electrical machine.
  • the MPC algorithm can also be supplied with control variables for optimization as input, in particular the speed of the vehicle (which can be proportional to the speed), the torque of the electrical machine and the battery charge state.
  • the MPC algorithm can deliver an optimal speed and an optimal torque for calculated points in the forecast horizon.
  • the MPC algorithm can be followed by a software module (“target generator”) which determines a currently relevant state and forwards it to power electronics.
  • the processor unit can be set up to take breaks for a driver of the vehicle into account as an arrival time factor when calculating the trajectory for the vehicle.
  • a break can be understood to mean that the vehicle is brought to a standstill, for example at a rest area or a parking lot, and that the vehicle is left at a standstill for a period of time (the break period).
  • Some of the breaks can be prescribed by law, for example for drivers of a truck or bus.
  • the breaks can be inserted before the start of the journey to the specified local final destination and / or during the journey to the specified local final destination and / or after the journey to the specified local final destination.
  • the location at which a break can be taken depends on the availability of parking spaces or rest areas along the motorway.
  • the time at which a break can be taken depends on how far the targeted parking space or rest area is from the current location of the vehicle and at what speed the vehicle can move to the targeted parking space or rest area or may.
  • the loading and unloading of a vehicle also plays a role in the speed planning for the vehicle in order to enable the vehicle to travel the entire route in the most energy-efficient and time-optimized manner possible.
  • the processor unit is set up in a further embodiment to take into account a time period for loading and / or unloading the vehicle, in particular a truck, as an arrival time factor when calculating the trajectory for the vehicle.
  • a period of time that the vehicle needs for refueling is also relevant. This includes the actual refueling process at a column, but also, for example, the times required to pay for the fuel and travel times from the actual route to a gas station and back to the actual route. Furthermore, a period of time for charging a battery is relevant, which provides electrical energy to drive the electric motor.
  • the processor unit is set up in a further embodiment to take into account a time period for refueling the vehicle and / or for charging a battery of the vehicle as an arrival time factor when calculating the trajectory for the vehicle.
  • information about the availability of corresponding charging stations for the battery can also be taken into account by the processor unit.
  • the processor unit can take into account traffic volume and / or congestion situations and / or weather conditions on the entire route to the specified destination as an arrival time factor as a secondary condition when determining the trajectory for the vehicle.
  • the processor unit carries out the advance calculation of driving speed and energy consumption, taking into account the specification of a desired arrival time. Alternatively or additionally, this can also be done taking into account the specification of a desired range.
  • the processor unit can be set up to take into account the availability of parking spaces at rest areas as an arrival time factor when calculating the trajectory for the vehicle.
  • the processor unit can be used to communicate with a depot (e.g. via Car2l) in order to reserve a time for loading and / or unloading the vehicle.
  • a depot e.g. via Car2l
  • the processor unit can be used to communicate with a depot (e.g. via Car2l) in order to reserve a time for loading and / or unloading the vehicle.
  • the drive train comprises an electrical machine and a battery.
  • the cost function can contain, as a first term, electrical energy weighted with a first weighting factor and predicted according to the longitudinal dynamics model, which is provided within the prediction horizon by a battery of the drive train to drive the electrical machine.
  • the cost function can contain, as a second term, a travel time weighted with a second weighting factor and predicted according to the longitudinal dynamics model, which the vehicle needs to cover the entire distance predicted within the prediction horizon.
  • the processor unit can do this be set up to determine an input variable for the electrical machine by executing the MPC algorithm as a function of the first term and as a function of the second term, so that the cost function is minimized.
  • This embodiment enables the target function or the cost function of a driving efficiency strategy to contain a further term in addition to the energy consumption, which also minimizes the driving time. This leads to the fact that, depending on the choice of weighting factors, a low speed is not always rated as optimal and thus there is no problem that the resulting speed is always at the lower limit of the permitted speed.
  • the cost function in one embodiment contains a final energy consumption value weighted with the first weighting factor, which the predicted electrical energy assumes at the end of the prediction horizon, and the cost function contains a final travel time value weighted with the second weighting factor, which the predicted travel time at the end of the prediction horizon.
  • the cost function can have a third term with a third weighting factor, the third term containing a value of a torque predicted according to the longitudinal dynamics model, which the electric machine provides for driving the motor vehicle, and the processor unit is set up to to determine the input variable for the electrical machine by executing the MPC algorithm as a function of the first term, as a function of the second term and as a function of the third term, so that the cost function is minimized.
  • the third term can contain a first value, weighted with the third weighting factor, of a torque predicted according to the longitudinal dynamics model, which the electric machine provides for driving the motor vehicle to a first waypoint within the prediction horizon.
  • the third term can contain a zero value of a torque weighted with the third weighting factor, which the electric machine provides for driving the motor vehicle to a zero waypoint which is immediately before the first waypoint.
  • the zeroth torque can in particular be a real - and not merely predicted - torque provided by the electrical machine. In the cost function, the zeroth value of the torque can be subtracted from the first value of the torque.
  • the third term can contain a first value, weighted with the third weighting factor, of a drive force predicted according to the longitudinal dynamics model, which the electric machine provides to drive the motor vehicle to a first waypoint within the prediction horizon.
  • the third term contains a zeroth value, weighted with the third weighting factor, of a driving force which the electric machine provides to drive the motor vehicle to a zeroth waypoint which is immediately before the first waypoint, with the zeroth value of the driving force in the cost function is subtracted from the first value of the driving force.
  • the waypoints which are taken into account by the MPC algorithm are, in particular, discrete waypoints which, for example, follow one another at a certain frequency.
  • the zeroth waypoint and the first waypoint represent discrete waypoints, with the first waypoint immediately following the zeroth waypoint.
  • the zeroth waypoint can be earlier than the prediction horizon.
  • the zeroth torque value can be measured or determined.
  • the first waypoint represents in particular the first waypoint within the prediction horizon.
  • the first torque value can be predicted for the first waypoint.
  • the zeroth torque value actually determined can thus be compared with the predicted first torque value.
  • torque gradients within the horizon that are too high are disadvantageous, so that in one embodiment they are already penalized in the objective function.
  • the square deviation of the driving force per meter can be weighted and minimized in the objective function.
  • the cost function can have a fourth term with a fourth weighting factor, the fourth term containing a gradient of the torque predicted according to the longitudinal dynamics model or an indicator value for a gradient of the torque predicted according to the longitudinal dynamics model.
  • the processor unit is thereby set up to machine the input variable for the electrical Ma by executing the MPC algorithm as a function of the first term, as a function of the second term, as a function of the third term and as a function of the fourth term determine so that the cost function is minimized.
  • the fourth term contains a quadratic deviation of the gradient of the torque multiplied by the fourth weighting factor and added up.
  • the cost function can contain a quadratic deviation, summed up with the fourth weighting factor, of a drive force which the electrical machine provides in order to move the motor vehicle one meter in the longitudinal direction.
  • the fourth term can contain a quadratic deviation of a driving force multiplied by the fourth weighting factor and added up, which the electric machine provides to move the motor vehicle one meter in the longitudinal direction.
  • Speed limits which can be set for example by traffic rules, are hard limits for optimization that should not be exceeded. In reality, it is always permissible to slightly exceed the speed limits and, above all, it is more the norm when passing from one speed zone to a second zone. In dynamic environments in which the speed limits shift from one computing cycle to the next, it can happen that no valid solutions are found in the case of very hard limits. solution for a speed curve can be found.
  • a so-called “soft constraint” can be introduced into the objective function.
  • a so-called “slip variable” or “slack variable” can become active in a predetermined narrow range before the hard speed limit is reached.
  • the cost function can contain a Slack variable weighted with a fifth weighting factor as the fifth term, the processor unit being set up to, by executing the MPC algorithm as a function of the first term, as a function of the second term, to determine the input variable for the electrical machine as a function of the third term, as a function of the fourth term and as a function of the fifth term, so that the cost function is minimized.
  • the tractive effort can be limited by restricting the electrical machine's map.
  • the battery is the limiting element for maximum recuperation.
  • a certain negative performance value should not be undershot.
  • a driver assistance system for a vehicle that is driven by means of an electric machine.
  • the driver assistance system is set up to access an input variable for the electrical machine by means of a communication interface, the input variable having been determined by a processor unit according to the first aspect of the invention.
  • the processor unit determines the input variable for the electrical machine by executing an MPC algorithm as a function of a first term and as a function of a second term of a cost function of the MPC algorithm.
  • the first term represents an electrical energy weighted with a first weighting factor and predicted according to the longitudinal dynamics model of the vehicle, which within a prediction horizon of the Battery is provided to drive the electric machine.
  • the second term represents a driving time weighted with a second weighting factor and predicted according to the longitudinal dynamics model, which the vehicle needs to cover the entire distance predicted within the prediction horizon.
  • the driver assistance system is set up to control the electrical machine based on the input variable.
  • a vehicle which comprises an electric machine, a battery and a driver assistance system according to the second aspect of the invention.
  • the vehicle is, in particular, a motor vehicle that is driven by a motor, for example an automobile (for example a passenger car with a weight of less than 3.5 t), motorcycle, scooter, moped, bicycle, e-bike or Pedelec (acronym for Pedal Electric Cycle), bus or truck (e.g. weighing more than 3.5 t), or a rail vehicle, a ship, an aircraft such as a helicopter or an airplane.
  • a motor vehicle for example an automobile (for example a passenger car with a weight of less than 3.5 t), motorcycle, scooter, moped, bicycle, e-bike or Pedelec (acronym for Pedal Electric Cycle), bus or truck (e.g. weighing more than 3.5 t), or a rail vehicle, a ship, an aircraft such as a helicopter or an airplane.
  • the invention can also be used in small, light electric micro-mobility vehicles, these vehicles being used in particular in urban traffic and for the first and last mile in rural areas.
  • the first and last mile can
  • the vehicle can, for example, belong to a vehicle fleet.
  • the vehicle can be controlled by a driver, possibly supported by a driver assistance system.
  • the vehicle can also be controlled remotely and / or (partially) autonomously, for example.
  • the vehicle can be an electric vehicle, a hybrid vehicle, or a plug-in hybrid vehicle.
  • An "electric vehicle” can be a vehicle be understood that is powered by electrical energy.
  • the electric vehicle can be supplied with drive energy in the form of electrical energy.
  • the electrical energy can be stored in a battery of the electric vehicle (Battery Electric Vehicle).
  • the electrical energy can in particular be supplied per manent from the outside, z. B. via a busbar, an overhead line or by induction.
  • a “hybrid vehicle” can be understood to mean an electric vehicle that can be driven by at least one electric motor and at least one internal combustion engine.
  • the hybrid vehicle can draw energy from a battery as well as from an additional fuel, such as diesel, gasoline or gas.
  • a “plug-in hybrid vehicle” can be understood to mean a hybrid vehicle whose battery can be charged both via an internal combustion engine and via a power grid that is located outside the plug-in hybrid vehicle and which has an electrical connection for the plug -in hybrid vehicle can be connected.
  • a plug-in hybrid vehicle can be viewed as a hybrid between a hybrid vehicle and an electric car.
  • a method for model-based predictive control of a vehicle taking into account an arrival time factor.
  • the procedure consists of the following steps:
  • a computer program product for model-based predictive control of a vehicle taking into account a Arrival time factor provided.
  • the computer program when it is executed on a processor unit, instructs the processor unit,
  • a trajectory for the vehicle taking into account at least one arrival time factor, the trajectory comprising an entire route to a specified destination at which the vehicle is to arrive, and the arrival time factor indicating an arrival time of the vehicle at the specified destination be influenced, and
  • an MPC algorithm ‘ which contains a longitudinal dynamics model of a drive train of the vehicle and a cost function to be minimized, to optimize a section of the trajectory for the vehicle for a sliding prediction horizon so that the cost function is minimized.
  • Fig. 1 is a side view of a vehicle with a drive train comprising an electric machine and a battery, and
  • Fig. 2 is a plan view of a road on which the vehicle of Fig. 1 can drive auto nom in order to get from a starting point to a destination point.
  • Fig. 1 shows a vehicle in the form of a motor vehicle 1, for example a passenger car (car) or a truck (truck).
  • the motor vehicle 1 comprises a system 2 for model-based predictive control of an electrical machine 8 of a drive train 7 of the motor vehicle 1, taking into account an arrival time Factor.
  • the system 2 comprises a processor unit 3, a memory unit 4, a communication interface 5 and a detection unit 6 for detecting status data relating to the motor vehicle 1.
  • the motor vehicle 1 furthermore comprises a drive train 7, which, for example, can comprise an electric machine 8, which can be operated as a motor and as a generator, a battery 9 and a transmission 10.
  • the electric machine 8 can drive wheels of the motor vehicle 1 via the transmission 10, which can have a constant gear ratio, for example.
  • the battery 9 can provide the necessary electrical energy.
  • the battery 9 can be charged by the electric machine 8 when the electric machine 8 is operated in generator mode (recuperation).
  • the battery 9 can optionally also be charged at an external charging station if the motor vehicle 1 is a plug-in hybrid vehicle.
  • the drive train of the motor vehicle 1 can also optionally have an internal combustion engine 21, which can drive the motor vehicle 1 as an alternative or in addition to the electric machine 8.
  • the internal combustion engine 21 can also be set up to drive the electric machine 8 in order to charge the battery 9.
  • a computer program product 11 can be stored on the storage unit 4.
  • the computer program product 11 can be executed on the processor unit 3, for which purpose the processor unit 3 and the memory unit 4 are connected to one another by means of the communication interface 5.
  • the computer program product 11 When the computer program product 11 is executed on the processor unit 3, it instructs the processor unit 3 to fulfill the functions described in connection with the drawing or to carry out method steps.
  • the computer program product 11 contains an MPC algorithm 13.
  • the MPC algorithm 13 in turn contains a longitudinal dynamics model 14 of the drive train 7 of the motor vehicle 1 and a cost function 15 to be minimized.
  • the processor unit 3 executes the MPC algorithm 13 and determines an optimized one Trajectory for the motor vehicle 1. A behavior of the motor vehicle 1 is predicted based on the longitudinal dynamics model 14, the cost function 15 being minimized.
  • the output of the optimization by the MPC algorithm 13 results, for example, in a speed matched to the optimized trajectory and an optimal torque of the electrical machine 8 for calculated points in the prediction horizon.
  • the processor unit 3 can determine an input variable for the electrical Ma machine 8 so that the optimal speed and the optimal torque are set.
  • the processor unit 3 can control the electrical machine 8 based on the determined input variable. However, this can also be done by a driver assistance system 16.
  • the detection unit 6 can measure current state variables of the motor vehicle 1, record corresponding data and feed them to the MPC algorithm 13.
  • route data from an electronic map for a forecast horizon or prediction horizon (e.g. 500 m) in front of the motor vehicle 1 can be updated or updated, in particular cyclically.
  • the route data can contain, for example, incline information, curve information and information about speed limits.
  • a curve curvature can be converted into a speed limit for the motor vehicle 1 via a maximum permissible transverse acceleration.
  • the detection unit 6 can be used to locate the motor vehicle 1, in particular via a GNSS signal generated by a GNSS sensor 12 for precise localization on the electronic map.
  • the processor unit 3 can access this information via the communication interface 5, for example.
  • the vehicle 1 is located on a parking space 17 which is adjacent to a street 18 which leads to a depot 19.
  • the course of the road 18 is shown interrupted due to its length.
  • a driver of the vehicle 1 can specify to the driver assistance system 16 that it wants to get from the parking lot 17 (start) to the depot 19 (destination), for example.
  • the driver assistance system 16 can provide an autonomous driving function for the vehicle 1 so that the vehicle 1 drives autonomously from the parking lot 17 to the depot 19.
  • the processor unit 3 or the driver assistance system 16 can first generate the entire route 20 from the parking lot 17 to the depot 19 and, given discrete waypoints on this route, assign a speed of the vehicle 1.
  • the trajectory for the vehicle 1 is calculated in this way.
  • the processor unit 3 or the driver assistance system 16 can use the acquisition unit 6 and a planning module for this purpose, which can be implemented as software, for example.
  • the planning module can comprise several levels, e.g. a navigation level on a larger scale (e.g. several km; here the trajectory for vehicle 1 can be selected from start 17 to destination 19) and a navigation level on a smaller scale (e.g. in the 50m to 100m ahead , depending on the speed of the vehicle 1; a choice of course and speed in the vicinity of the vehicle 1 is possible here in order to determine how the vehicle 1 should move in traffic).
  • arrival time factors which influence an arrival time of the vehicle 1 at the specified destination 19 can be taken into account.
  • breaks for the driver (for example a private person, a truck driver or a bus driver) of the vehicle 1 are taken into account.
  • a period of time for loading the vehicle 1, in particular if it is a truck, as well as times for refueling and / or charging the battery 9 of the vehicle 1 can be taken into account.
  • information about the availability of a charging station 22 for the battery 9 of the vehicle 1, about available parking spaces 23 at rest areas 24 for trucks can be taken into account.
  • information about a traffic volume and traffic jams as well as weather conditions on route 20 can be taken into account.
  • the processor unit 3 of the vehicle 1 can also communicate with a processor unit 25 of the depot 19 (e.g. via a car2l communication) in order to reserve a point in time for loading and / or unloading that matches the calculated trajectory for the vehicle 1 can be. This enables a reduced waiting time and instead a use of the time for more energy-efficient driving.
  • the processor unit 3 executes the MPC algorithm 13 and optimizes it for a sliding (ie spatially or in the way shifting) prediction horizon a current section of the trajectory for the vehicle 1, so that the cost function is minimized.
  • the processor unit 3 can form the planning module (“top level” planning module) described above, which plans the entire route 20 and trajectory for the vehicle 1, taking into account the mentioned arrival time factors.
  • This planning module can then transfer sections or parts of the vehicle trajectory for the entire route to the MPC algorithm 13, by means of which an optimal trajectory of the vehicle 1 within the prediction horizon can be determined.
  • the processor unit 3 can also take into account an arrival time specified by the driver of the vehicle 1 at the specified destination 19 or a range specified by the driver of the vehicle 1 as a secondary condition when optimizing the trajectory for the vehicle 1.
  • An exemplary longitudinal dynamics model 14 of the motor vehicle 1 can be expressed mathematically as follows:
  • v is the speed of the motor vehicle
  • Fd is the drag force of the motor vehicle
  • meq is the equivalent mass of the motor vehicle
  • the equivalent mass includes in particular the inertia of the rotating parts of the drive train that are exposed to the acceleration of the motor vehicle (engine, transmission drive shafts, wheels).
  • the dynamics equation of the longitudinal dynamics model 14 is linearized by dekin expressing the speed through coordinate transformation using kinetic energy.
  • the quadratic term for calculating the air resistance Fd is replaced by a linear term and at the same time the longitudinal dynamics model 14 of the motor vehicle 1 is no longer described as a function of time as usual, but as a function of the path. This fits well with the optimization problem insofar as the forecast information of the electrical horizon is path-based.
  • the electrical energy consumption of the drive train 7 is usually described in the form of a map as a function of torque and engine speed.
  • the motor vehicle 1 has a fixed transmission ratio between the electrical machine 8 and the road on which the motor vehicle 1 is moving. This allows convert the speed of the electric machine 8 directly into a speed of the motor vehicle 1 or even into a kinetic energy of the motor vehicle 1.
  • the electrical power of the electrical machine 8 can be converted into energy consumption per meter by dividing the corresponding speed. In order to be able to use a corresponding map of the electrical machine 8 for the optimization, it is approximated linearly: Your gy per meter > a £ * k i n + bi * F trac for all i.
  • the cost function 15 to be minimized can be expressed mathematically as follows:
  • FA Driving force which is provided by the electric machine, is constantly translated by a transmission and is applied to a wheel of the motor vehicle
  • the cost function 15 has only linear and quadratic terms.
  • the overall problem has the form of a quadratic optimization with linear constraints and a convex problem results, which can be solved quickly and easily.
  • the cost function 15 contains as the first term an electrical energy Eßat weighted with a first weighting factor Wßat and predicted according to the longitudinal dynamics model, which is provided within a prediction horizon by the battery 9 of the drive train 7 for driving the electric machine 8.
  • the cost function 15 contains a driving time T weighted with a second weighting factor WTime and predicted according to the longitudinal dynamics model 14, which the motor vehicle 1 needs to cover the predicted distance speed is not always rated as optimal and so there is no longer the problem that the resulting speed is always at the lower limit of the permitted speed.
  • the energy consumption and travel time can be evaluated and weighted at the end of the horizon. These terms are then only active for the last point on the horizon.
  • the quadratic deviation of An Driving force per meter is weighted with a weighting factor WTem and minimized in the cost function.
  • the torque MEM provided by the electrical machine 8 can also be used and weighted with the weighting factor WTem, so that the alternative term w Tem results. Due to the constant ratio of the gear 10 the driving force and the torque are directly proportional to each other.
  • speed limits are hard limits that must not be exceeded. Slightly exceeding the speed limits is always permissible in reality and is more the norm, especially when passing from one speed zone to a second zone. In dynamic environments, where speed limits shift from one computing cycle to the next, it can happen that if the limits are very hard, no valid solution can be found for a speed curve.
  • a soft constraint is introduced into the cost function 15.
  • Varsiack weighted with a weighting factor Ws lack becomes active in a predetermined narrow range before the hard speed limit is reached. Solutions that are very close to this speed limit are rated worse, that is, solutions whose speed trajectory keep a certain distance from the hard limit.

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Hybrid Electric Vehicles (AREA)

Abstract

L'invention se rapporte au contrôle prédictif basé sur un modèle d'un véhicule prenant en compte un facteur de temps d'arrivée. Il est prévu en particulier une unité de traitement qui est conçue pour calculer une trajectoire du véhicule (1) en prenant en compte au moins un facteur de temps d'arrivée, la trajectoire comprenant un itinéraire complet (20) jusqu'à une destination prédéfinie (19) à laquelle il est prévu que le véhicule (1) arrive, et le facteur de temps d'arrivée influençant un temps d'arrivée du véhicule (1) à la destination prédéfinie (19). L'unité de traitement est en outre conçue pour optimiser une section de la trajectoire du véhicule (1) en exécutant un algorithme MPC qui contient un modèle de dynamique longitudinale d'une chaîne cinématique du véhicule (1) et une fonction de coût à réduire à un minimum pour un horizon de prédiction coulissante de sorte à réduite à un minimum la fonction de coût.
PCT/EP2020/055771 2020-03-05 2020-03-05 Contrôle prédictif basé sur un modèle d'un véhicule prenant en compte un facteur de temps d'arrivée WO2021175423A1 (fr)

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US17/776,946 US20220402476A1 (en) 2020-03-05 2020-03-05 Model-Based Predictive Control of a Vehicle Taking into Account a Time of Arrival Factor
CN202080079801.5A CN114746316A (zh) 2020-03-05 2020-03-05 在考虑到到达时间因素的情况下对交通工具的基于模型的预测控制
PCT/EP2020/055771 WO2021175423A1 (fr) 2020-03-05 2020-03-05 Contrôle prédictif basé sur un modèle d'un véhicule prenant en compte un facteur de temps d'arrivée

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